应用简介
基于测量的模型路由器,用于Claude代码。根据任务类别路由Haiku/Sonnet/Opus,以真实数字记录每个路由的任务,并在您信任节省之前对更便宜的层级进行A/B测试。
--- name: tokenwise description: "Measurement-driven model router for Claude Code. Routes Haiku/Sonnet/Opus per task class, logs every routed task with real $ numbers, and A/B tests cheaper tiers before you trust the savings." category: developer-tools risk: safe source: community source_repo: CodeShuX/tokenwise source_type: community date_added: "2026-05-12" author: CodeShuX tags: [model-routing, token-optimization, cost-reduction, anthropic, haiku, sonnet, opus, claude-code, ab-testing, measurement] tools: [claude] license: "MIT" license_source: "https://github.com/CodeShuX/tokenwise/blob/main/LICENSE" --- # TokenWise — Measurement-Driven Model Router ## Overview A Claude Code skill that auto-routes subtasks to the cheapest model that can handle them (Haiku for grunt work, Sonnet for scoped reasoning, Opus only for synthesis), then logs every routed task to a local NDJSON with real token + cost numbers. Includes an A/B test subcommand that runs the same task across multiple tiers and scores quality, so the routing decisions are verified against the user's real workload — not estimated. Anthropic's own bug tracker (Issue #27665) reports 93.8% of Max-subscriber Claude Code tokens flow to Opus. Existing routers (claude-router, wshobson, VoltAgent) either pin models statically or route by vibes-based heuristics with no measurement. TokenWise fills the measurement gap. ## When to use - Cutting Claude Code token spend without sacrificing output quality - Validating whether Haiku/Sonnet is "good enough" for a specific task class before trusting auto-routing - Auditing where Opus tokens are actually being burned - Logging per-session cost data for finance or chargeback ## Subcommands - `/tokenwise:install` — guided installer with diff preview, automatic backups, and `--dry-run` mode - `/tokenwise:report` — per-session token + cost summary vs all-Opus baseline - `/tokenwise:summary [--week|--month|--all]` — historical aggregate with trend - `/tokenwise:ab "<task>"` — A/B test the same task at multiple tiers, generates a markdown comparison - `/tokenwise:undo` — restore CLAUDE.md / settings.json from backup ## Routing taxonomy | Tier | Model | Task class | |---|---|---| | Mechanical | Haiku 4.5 | file reads, grep, format, rename, simple edits, doc lookups | | Scoped reasoning | Sonnet 4.6 | single-file refactor, scoped research, test writing | | Synthesis | Opus 4.7 | architecture decisions, multi-file refactor, security review | Safety caps: - Haiku never spawns further subagents - Max spawn depth = 2 - Subagents that need a smarter model return to parent — they never escalate on their own - Tasks under 100 chars with no file context run inline (subagent overhead > savings) - Subagent context >30k tokens bumps a tier ## Privacy Zero telemetry. All logs in `.tokenwise/log.ndjson` local to the project. Task descriptions truncated to 80 chars and stripped of file contents before logging. No analytics endpoint exists in the source. ## Install In any Claude Code session: ``` /plugin marketplace add CodeShuX/tokenwise /plugin install tokenwise@tokenwise ``` Then run `/tokenwise:install` and follow the guided prompts. ## Limitations - Token counts approximate to ±2% vs Anthropic billing - A/B test mode costs extra tokens (one task × N tiers) — intentional one-time validation - Anthropic-only by design (use LiteLLM or OpenRouter for cross-vendor) - Subagent `model:` param has known silent-fail bugs on some Claude Code builds — skill probes for this at install and refuses to configure if routing is broken ## Source - Repo: https://github.com/CodeShuX/tokenwise - License: MIT - Author: CodeShuX
发布日期
5/16/2026
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SkillOPIC
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sickn33
coding
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